Adhiyaman Srinivasan

AI Developer

Chennai, IN.

About

2nd year CSE Student @VITC — Focused on Machine Learning, MLOps, AI Agents, Business & Data Analytics, and existential poetry.

Hands-on experience in developing and optimizing generative AI models, large language model (LLM) based multi-agent systems, and data-driven solutions. Proven ability to enhance workflow efficiency, improve output accuracy, and build scalable applications through expertise in Python, machine learning frameworks, and data analytics.

Crafting stories, poetry, and philosophical reflections.
Grounded through volleyball, football, and table tennis.

Work

Yoto - Agentic AI Product Development Company

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AI Developer Intern

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Summary

Contributed to the development of cutting-edge AI products, focusing on multi-agent systems and generative AI model optimization, enhancing product capabilities and team knowledge.

Highlights

Developed multi-agent systems leveraging Large Language Models (LLMs) for automated task execution and decision-making, significantly improving workflow efficiency by 75%.

Fine-tuned generative AI models to enhance document analysis and productivity tools, leading to improved output accuracy.

Built intelligent data pipelines using internal tools to streamline agent coordination and optimize operational workflows.

Documented complex architectures and experimental findings in wikis.

Education

Vellore Institute of Technology, Chennai

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M.Tech Integrated Computer Science and Engineering with Business Analytics Chennai, India

Srimathi Sundaravalli Memorial School

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Higher Secondary Perungalathur, Chennai, India

Skills

Frameworks & Libraries

Scikit-learn, TensorFlow, Keras, FastAPI, Flask, OpenCV.

Programming Languages

Python, NumPy, Pandas, Matplotlib, Seaborn, SQL, PostgreSQL, Supabase, JavaScript, HTML, CSS, C/C++.

AI/ML Domains

Machine Learning, Generative AI, Large Language Models (LLMs), Prompt Engineering, Data Analytics, Workflow Automation.

Dev Tools

Git, GitHub, Docker, Jupyter Notebooks, Google Colab, Cursor.

Projects

Movie Recommendation System

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Summary

Developed a content-based movie recommendation system leveraging machine learning techniques and a public dataset to provide personalized movie suggestions.